Thanks for the feedback. My plan is to spend a little more time to dive into the details on a follow up post.
I'll try to explain our experience here in a little better detail though.
In a traditional IAC tool (tf for example). The flow would go something like this (YMMV)
Update TF -> Plan -> PR -> Review (auto or peer) -> Merge -> TF Reviews State File -> TF Makes changes -> Updates State.
Some issues we could run into:
- We support multiple customers each with their own teams that may or may not have updated infra so drift is always present.
- We support customers over time so modules and versions age, and we aren't always given the time to go make sure that past tf is updated. So version pins need to be updated among other dependencies.
Each of those could take a bit of time to resolve so that the tf plans clean and our updates are applied. Of course there are tools such as HCP Cloud, Spacelift, Terrateam etc. But, in my experience it shifts a lot of the same problems to different parts of the workflow.
The work flow with SI is closer to the following:
Ask AI for a change -> AI builds a changeset (PR) -> Review -> Apply
The secret sauce is SI's "digital twin". We aren't just using AI to update code, we're actually using it to initiate changes to AWS via SI. While I would never want to have a team make changes directly to AWS without a peer review or something similar, it is sitting closer to what the actual infrastructure is. Even with changes that are happening to the infrastructure naturally.
This has allowed us to move quite a bit faster in updating and maintaining our customers infrastructure. While still sticking as close as possible to best practices.
I'll try to explain our experience here in a little better detail though.
In a traditional IAC tool (tf for example). The flow would go something like this (YMMV)
Update TF -> Plan -> PR -> Review (auto or peer) -> Merge -> TF Reviews State File -> TF Makes changes -> Updates State.
Some issues we could run into: - We support multiple customers each with their own teams that may or may not have updated infra so drift is always present.
- We support customers over time so modules and versions age, and we aren't always given the time to go make sure that past tf is updated. So version pins need to be updated among other dependencies.
Each of those could take a bit of time to resolve so that the tf plans clean and our updates are applied. Of course there are tools such as HCP Cloud, Spacelift, Terrateam etc. But, in my experience it shifts a lot of the same problems to different parts of the workflow.
The work flow with SI is closer to the following: Ask AI for a change -> AI builds a changeset (PR) -> Review -> Apply
The secret sauce is SI's "digital twin". We aren't just using AI to update code, we're actually using it to initiate changes to AWS via SI. While I would never want to have a team make changes directly to AWS without a peer review or something similar, it is sitting closer to what the actual infrastructure is. Even with changes that are happening to the infrastructure naturally.
This has allowed us to move quite a bit faster in updating and maintaining our customers infrastructure. While still sticking as close as possible to best practices.